The Katalon Blog

How To Calculate Test Automation ROI?

Written by Katalon Team | Jul 29, 2024 5:00:00 AM

Test automation is no longer a cutting-edge technology. It is now a standard for any QA team looking to bring product quality to the next level. However, some testers are hesitant. They pose questions: does test automation actually bring ROI? Or perhaps they’re wary of the struggle with maintaining automation test scripts that break every time a new update is rolled out.
 

It is those questions that deter business leaders from making the final decision, but they have a reason to be wary. For a data-driven and informed investment decision in automation testing, we first need to quantify the value test automation brings. ROI is the perfect metric for this.
 

In this article, we’ll show you how to calculate test automation ROI and determine a concrete number on the impact of test automation.
 

How Test Automation Improves ROI?

Here are 5 key reasons why ROI can be improved when you transition from manual testing to automation testing:
 

  1. Cost-saving: this is the biggest reason. Adding more manual testers to the team is not the best solution to ensuring quality for increasing complexity of the system-under-test. Manual testing is quite time-consuming and has lower scalability. Instead, a hybrid approach is usually recommended. With this approach, all of the repetitive test cases are automated, while test cases that are more exploratory and ad-hoc by nature are still done manually. The result is higher efficiency.
     
  2. Increased test coverage thanks to CI/CD integration: since all of the repetitive test cases are conveniently automated, testers can focus on the more elusive bugs which require chains of interactions with the system to trigger. This improves test coverage. Moreover, automated scripts can be seamlessly integrated with the CI/CD systems to help execute the code immediately, speeding the process up.
     
  3. Consistent results: manual testers can produce inconsistent test results, since each tester performs the test case in their own unique way. It is also difficult for the devs to reproduce the exact bug if there is not enough documentation or screenshots as evidence. Automation testing solves this problem by bringing a level of standardization to the process. All steps are executed in the same way, minimizing the risks of false positives and overall accuracy.
     
  4. Better resource optimization: automation allows skilled testers to focus on more complex and exploratory testing rather than repetitive tasks. More importantly, automated tests can be scheduled to run around the clock.

Learn More: How to switch from manual to automation testing? A step-by-step guide

 

Test Automation ROI Formula

Test automation ROI formula is the amount of return on an investment into test automation relative to its initial cost.
 

By that definition, we have the formula:
 

Test Automation ROI = ((Return − Initial Investment)​ / Initial Investment) × 100
 

where:

  • Return is the total financial gain or benefit obtained from the test automation.
  • Initial Investment includes all the upfront costs required to implement test automation, such as tools, training, and setup costs.

 

Steps To Calculate ROI

Step 1. Choose a timeframe

The first step is to decide on a period over which the ROI will be calculated (e.g., 6 months, 1 year). We will compare the cost before vs after adopting the test automation solution. 1 year is usually the recommended time-frame 

Step 2. Calculate the amount of initial investment into test automation

When a manual testing team wants to transition to automation testing, they have 2 options: build vs buy. Either they build a fully customized tool in-house from scratch or buy a tool from a vendor.
 

If they want the Buy option, they can simply shop around for an automation testing tool. These tools are either purchased one-time or on a subscription basis.
 

If they want the Build option, it’s another story. To build an automation testing tool/framework, QA teams need:

  1. Infrastructure cost: any hardware (physical machines/servers) and cloud services (AWS, Azure, Google Cloud, etc.) required to run the automation tests. Considering the vast array of devices on the current market, it is recommended that QA teams find a balance between cloud-based testing and real device testing. For example here some several costs that you can immediately consider:
    1. Physical servers
    2. Dedicated testing machines
    3. Networking equipment for performance testing
    4. Storage devices
    5. Backup and recovery hardware
    6. Cloud service instance
    7. Cloud storage costs
    8. VPC cost
    9. Data transfer cost
    10. Security services
    11. Operating system license cost
    12. Virtualization software cost
       
  2. Development cost: to build a framework from scratch, technical expertise is essential. Included in development cost is salaries for developers/testers involved in the process of setting up scripts. If teams decide to outsource or onboard external teams to another layer of expertise, make sure to account consulting/outsourcing fees. Here’s a quick list for your team:
    • Design and architecture planning
    • Initial setup of test automation frameworks (e.g., Selenium, Appium)
    • Integration with CI/CD tools (Jenkins, GitLab CI, CircleCI)
    • Configuration of reporting tools (Allure, ExtentReports)
    • QA engineer salary
    • DevOps engineer salary
    • Project manager salary
    • Third-party consultant fee
    • Cost of internal training programs
    • Cost of external training courses and certifications
    • Cost of training materials (books, software)
    • Fees for hiring external trainers
       
  3. Ongoing cost: once the framework is fully developed, we also need to continuously maintain the framework. The costs here include server upkeep, cloud service fees, and other infrastructure-related expenses. Several examples are:
    • Salaries of staff responsible for script maintenance
    • Salaries of staff for updating test cases
    • Salaries of staff for integrating new features into the framework
    • Cost of time spent fixing automation script bugs
    • Cost of time spent updating scripts for new features or changes
    • Cost of time spent on regression testing and ensuring stability
    • Costs for maintaining test environments
    • Costs for periodic data refreshes

Step 3. Calculate the return from test automation

If you have not yet adopted the tool, make sure to research industry standards and case studies to find data on typical ROI from test automation in similar companies or projects. These benchmarks help you estimate potential savings and returns.
 

The return from test automation is potential savings from automating tests. Start with identifying your manual testing costs, then calculate your automation testing cost. The difference between them is the return from test automation.
 

Here’s a quick table for you to visualize it:
 

State

Timeframe

Area

Cost

Total

Manual testing

Year 1

Configuration

$300,000

$2,100,000

Maintenance cost

$900,000

Year 2

Maintenance cost

$900,000

Automation testing

Year 1

Configuration

$7,000

$607,000

Maintenance cost

$300,000

Year 2

Maintenance cost

$300,000

Return

$1,493,000

 

After that, apply the ROI calculation formula for test automation:
 

Test Automation ROI = ((Return − Initial Investment)​ / Initial Investment) × 100

= ((1,493,000 - 607,000) / 607,000) x 100

= 145.9%

 

 

Common Challenges When Calculating Test Automation ROI

  1. It is challenging to measure the more intangible benefits of automation. It is up to the team to define those intangible metrics such as improved product quality, although they are definitely affected by test automation. A good solution is the Cost Approach where we calculate the cost required to deliver the same level of quality with manual testing compared with using automation testing.
     
  2. There are external factors that affect the variables under consideration. For example, the rate at which new testing technologies and practices are adopted can affect the effectiveness of automation. High adoption rates may lead to better tools and practices but also require frequent updates and learning.